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RNA solvent accessibility, similar to protein solvent accessibility, reflects the structural regions that are accessible to solvents or other functional biomolecules, and plays an important role for structural and functional characterization. Unlike protein solvent accessibility, only a few tools are available for predicting RNA solvent accessibility despite the fact that millions of RNA transcripts have unknown structures and functions. Also, these tools have limited accuracy. Here, we have developed RNAsnap2 that uses a dilated convolutional neural network with a new feature, based on predicted base-pairing probabilities from LinearPartition.

Single-sequence and profile-based prediction of RNA solvent accessibility using dilated convolutional neural network